Inspired by the ideas of curriculum learning [1]}, we propose a progressive training mechanism where the network gradually receives more deteriorated speech samples during training iterations and not randomly from the beginning of the optimization process. Based on the literature, we expect the models trained on this regime to have better generalization capabilities, which can lead us to reinforce the model behavior in learning speech features disentangled from noise. In our work, we gradually increase the difficulty of the online contamination process by defining a custom sampling procedure instead of allowing every scenario from the beginning. This process is done until we reach half of the training steps, and after, the same regime of the previous section is applied. The pseudocode for this step is illustrated in the Supplementary Material Section.
